Football is undoubtedly one of the sports in which data is most widely used. It is now possible to find data for every type of action and play. A large proportion of this data is accessible to the general public via api or packages. In this project, we are going to use the R package worldfootballR to visualise and analyse team shots in the five major European leagues. In the first part, we will visualise the position of the teams’ shots using heat maps. We will then analyse the ratio of shots to goals scored in the five leagues studied.
This study is based on data from the five European leagues from the start of this season (2023-24) to 30/11/2023.
In this section, we’re going to try and create a visualisation of the teams’ shots in the form of a heat map. This type of visualisation is often used because it’s easy to understand and produce. This work will also allow us to demonstrate how easy it is to create a heat map once we have the data.
The data is imported using the
understat_league_season_shots function. The data is
retrieved from the Understat site.
#bundesliga <- understat_league_season_shots("Bundesliga", 2023)
#ligue1 <- understat_league_season_shots("Ligue 1", 2023)
#Liga <- understat_league_season_shots("La liga", 2023)
#Premier_League <- understat_league_season_shots("EPL", 2023)
#Serie_A <- understat_league_season_shots("Serie A", 2023)bundesliga <- read.csv("data/bundesliga.csv")
ligue1 <- read.csv("data/ligue1.csv")
Liga <- read.csv("data/Liga.csv")
Premier_League <- read.csv("data/Premier_League.csv")
Serie_A <- read.csv("data/Serie_A.csv")Once the data has been imported, we transform it into a ‘tibble’ object to make it easier to manipulate.
bundesliga <- as_tibble(bundesliga)
ligue1 <- as_tibble(ligue1)
Liga <- as_tibble(Liga)
Premier_League <- as_tibble(Premier_League)
Serie_A <- as_tibble(Serie_A)This is what one of our five datasets looks like (each dataset has the same structure):
head(bundesliga)## # A tibble: 6 × 21
## league id minute result X Y xG player h_a player_id
## <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <chr> <chr> <int>
## 1 Bundesliga 532854 22 SavedShot 0.741 0.569 0.0594 Marvi… h 4329
## 2 Bundesliga 532862 45 MissedShots 0.84 0.48 0.0255 Mitch… h 28
## 3 Bundesliga 532864 46 MissedShots 0.92 0.45 0.422 Leona… h 262
## 4 Bundesliga 532865 48 MissedShots 0.893 0.557 0.0880 Nicla… h 6098
## 5 Bundesliga 532870 62 MissedShots 0.781 0.503 0.0322 Jens … h 10734
## 6 Bundesliga 532879 91 MissedShots 0.759 0.567 0.0130 Roman… h 9069
## # ℹ 11 more variables: situation <chr>, season <int>, shotType <chr>,
## # match_id <int>, home_team <chr>, away_team <chr>, home_goals <int>,
## # away_goals <int>, date <chr>, player_assisted <chr>, lastAction <chr>Here are the game situations taken into account in our data:
unique(bundesliga$situation)## [1] "DirectFreekick" "OpenPlay" "SetPiece" "FromCorner"
## [5] "Penalty"We save the team names in vectors to make it easier to visualise them later.
bundesliga_teams <- unique(bundesliga$home_team)
ligue1_teams <- unique(ligue1$home_team)
Liga_teams <- unique(Liga$home_team)
Premier_League_teams <- unique(Premier_League$home_team)
Serie_A_teams <- unique(Serie_A$home_team)bundesliga_teams## [1] "Werder Bremen" "Bayer Leverkusen" "Wolfsburg"
## [4] "Hoffenheim" "Augsburg" "VfB Stuttgart"
## [7] "Borussia Dortmund" "Union Berlin" "Eintracht Frankfurt"
## [10] "RasenBallsport Leipzig" "Freiburg" "FC Cologne"
## [13] "Bochum" "FC Heidenheim" "Darmstadt"
## [16] "Borussia M.Gladbach" "Mainz 05" "Bayern Munich"To create the heat map function, two elements are important:
annotate_pitch and theme_pitch
functions.We will also represent the goals on the heat map using black dots.
# Colors for the heat map
custom_palette <- c("transparent", "green", "yellow", "orange", "red")
# Heat map function
heat_map_shots <- function(team, league, df) {
data_home <- filter(df, df$home_team == team & df$h_a=="h")
data_away <- filter(df, df$away_team == team & df$h_a=="a")
data <- bind_rows(data_home, data_away)
logo <- readPNG(sprintf("logos/%s/%s.png",league,team))
p <- ggplot(data, aes(x=X*100, y=Y*100) ) +
annotate_pitch(colour = "white",
fill = "springgreen4",
limits = FALSE,
linewidth = 1) +
theme_pitch() +
theme(panel.background = element_rect(fill = "springgreen4")) +
stat_density_2d(aes(fill = ..density..), geom = "raster", contour = FALSE) +
scale_fill_gradientn(colors = custom_palette, guide = "none") +
geom_point(data =filter(data, result=="Goal"), aes(X*100, Y*100), color="black")+
coord_flip(xlim = c(49, 101)) +
ggtitle(team)+
theme(
plot.title = element_text(hjust = 0.5, size = 20, face = "bold")
)+
annotation_custom(rasterGrob(logo, width = unit(1, "npc"), height = unit(1, "npc")),
xmin= 55,xmax = 65, ymin = 85, ymax = 95)
return(p)
}This is what our heat map looks like:
heat_map_shots("Bayern Munich", "bundesliga", bundesliga)
On this heat map, the colour variation represents the density of shots taken by the team and the black dots represent the goals scored by the team.
Now we’re going to create a global representation for each league where we can see the heat map for each club.
We’re going to create two representations that will serve as the legend for our global representation.
# Legend goals
data_legend_goal <- data.frame(
x = c(1),
y = c(1)
)
legend_goal <- ggplot(data_legend_goal, aes(x=x,y=y))+
geom_point(aes(size=c(7))) +
ylim(-1.2,1.2) +
xlim(-4,6) +
theme_minimal() +
ggtitle("Goal")+
theme(
panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
legend.position = "none"
)
# Legend shots
df <- data.frame(value = c(75),
group = c(1))
df_expanded <- df %>%
rowwise() %>%
summarise(group = group,
value = list(0:value)) %>%
unnest(cols = value)
legend_shots <- df_expanded %>%
ggplot() +
geom_tile(aes(
x = group,
y = value,
fill = value,
width = 0.9
)) +
coord_flip() +
scale_fill_gradientn(colors = custom_palette, guide = "none") +
theme(legend.position = "none") +
xlim(0,2) +
theme_minimal() +
ggtitle("Shots density")+
theme(
panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, face = "bold")
)For each league, we create a list with the heat map for each team, to which we add the two legend representations.
# Bundesliga
plots_bundesliga <- lapply(bundesliga_teams, function(i) {
heat_map_shots(i, "bundesliga", bundesliga)
})
plots_bundesliga <- c(list(legend_goal, legend_shots), plots_bundesliga)
# Ligue 1
plots_ligue1 <- lapply(ligue1_teams, function(i) {
heat_map_shots(i, "Ligue_1", ligue1)
})
plots_ligue1 <- c(list(legend_goal, legend_shots), plots_ligue1)
# Premier League
plots_Premier_League <- lapply(Premier_League_teams, function(i) {
heat_map_shots(i, "Premier League", Premier_League)
})
plots_Premier_League <- c(list(legend_goal, legend_shots), plots_Premier_League)
# La Liga
plots_Liga <- lapply(Liga_teams, function(i) {
heat_map_shots(i, "liga", Liga)
})
plots_Liga <- c(list(legend_goal, legend_shots), plots_Liga)
# Serie A
plots_Serie_A <- lapply(Serie_A_teams, function(i) {
heat_map_shots(i, "Serie A", Serie_A)
})
plots_Serie_A <- c(list(legend_goal, legend_shots), plots_Serie_A)Here are the global heat map representations for each team in our five championships.
These representations are very interesting because they give us an idea of how each team attacks the goal. Some teams, such as Atletico Madrid, will shoot very close to goal, while others, such as Lecce, will diversify their shooting zones and take many of their shots from outside the box. These representations also allow us to see that some teams score almost exclusively from one side of the pitch, such as Liverpool (left), or from several areas inside and outside the box, such as Naples.
In this section, we’ll look at each team’s efficiency in front of goal by looking at their shots/goals scored ratio.
The function returns a dataframe with the number of shots and goals for each team in a league.
df_sg <- function(df, teams, league){
shots <- c()
goals <- c()
LogoPath <- c()
for(team in teams){
data_home <- filter(df, df$home_team == team & df$h_a=="h")
data_away <- filter(df, df$away_team == team & df$h_a=="a")
data_team <- bind_rows(data_home, data_away)
shots <- c(shots, nrow(data_team))
goals <- c(goals, nrow(filter(data_team, result=="Goal")))
LogoPath <- c(LogoPath, sprintf("logos/%s/%s.png",league, team))
}
sg <- tibble(team = teams, shots, goals, logos = LogoPath)
sg <-column_to_rownames(sg, var="team")
return(sg)
}df_sg_bundesliga <- df_sg(bundesliga, bundesliga_teams, "bundesliga")
df_sg_ligue_1 <- df_sg(ligue1, ligue1_teams, "Ligue_1")
df_sg_premier_league <- df_sg(Premier_League, Premier_League_teams, "Premier League")
df_sg_liga <- df_sg(Liga, Liga_teams, "liga")
df_sg_serie_a <- df_sg(Serie_A, Serie_A_teams, "Serie A")head(df_sg_ligue_1)## shots goals logos
## Marseille 170 12 logos/Ligue_1/Marseille.png
## Nice 171 13 logos/Ligue_1/Nice.png
## Brest 182 13 logos/Ligue_1/Brest.png
## Paris Saint Germain 216 34 logos/Ligue_1/Paris Saint Germain.png
## Nantes 166 17 logos/Ligue_1/Nantes.png
## Clermont Foot 156 8 logos/Ligue_1/Clermont Foot.pngplot_sg <- function(df, league, size=.1){
ggplot(df, aes(shots, goals)) +
geom_smooth(method=lm, color="red", fill="blue", se=TRUE) +
geom_image(aes(image=logos), size=size) +
ggtitle(sprintf("Shots / Goals comparison %s", league)) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 15),
axis.title.y = element_text(hjust = 0.5, size = 15),
axis.line = element_line(colour = "black"),
axis.text.x = element_text(face = "bold"),
axis.text.y = element_text(face = "bold")
)
}
These representations are very interesting and show that the more shots you take, the more you score. However, this is not the case for all teams. In Ligue 1, Clermont and Lyon lack realism and are average in terms of shots, but are the two worst teams in terms of goals scored. In the Premier League, Newcastle are exceptionally realistic, with the tenth-most shots on goal but the second-most goals scored. This phenomenon also applies in La Liga with Atletico Madrid and Girone. These two teams, who are in the top three in the Spanish league, take almost as many shots as the 18th-placed Celta Vigo. In Germany and Italy, the ratio of shots to goals is fairly similar for almost all the teams.
df_sg_all <- bind_rows(df_sg_bundesliga, df_sg_liga, df_sg_ligue_1, df_sg_premier_league, df_sg_serie_a)
Now, when we compare the shots/goals ratio between all the teams in the five European leagues, we can see that Bayer Leverkusen, Bundesliga leaders, are one of the European teams with the most realism. The fact that we didn’t notice this team in our first representations may testify to the realism of German teams in front of goal. However, we need to take a step back from this representation, because at this point in the season, the number of matches played by the teams differs according to the league (Ligue 1: 12 matches, Bundesliga: 12 matches, Premier League: 13 matches, Serie A: 13 matches, La Liga: 14 matches).
Finally, we can produce a ranking of realism in front of goal. To do this, we’re going to rank the teams according to their number of goals divided by the number of shots.
df_sg_all["ratio"] = df_sg_all$goals/df_sg_all$shotsHere are the 10 teams with the best goalscoring record:
head(arrange(df_sg_all, desc(ratio))[c("ratio")],10)## ratio
## Newcastle United 0.1812865
## Bayer Leverkusen 0.1794872
## Bayern Munich 0.1757322
## Atletico Madrid 0.1734104
## RasenBallsport Leipzig 0.1676301
## Girona 0.1666667
## VfB Stuttgart 0.1623037
## Paris Saint Germain 0.1574074
## Manchester City 0.1534884
## Hoffenheim 0.1509434In this ranking, we find at the top some of the over-performing teams mentioned earlier.
Here are the ten teams who were least realistic in front of goal:
head(arrange(df_sg_all, ratio)[c("ratio")],10)## ratio
## Lyon 0.04790419
## Udinese 0.04907975
## Clermont Foot 0.05128205
## Empoli 0.05755396
## FC Cologne 0.05769231
## Alaves 0.05820106
## Verona 0.06382979
## Bochum 0.06432749
## Celta Vigo 0.06842105
## Sheffield United 0.06896552As with the top of the table, here we find some of the teams who were previously singled out for their lack of realism.
In conclusion, we were able to show how easy it is to create and analyse team shot representations for the five major European leagues. Analyses of the teams’ shots/goals ratios have shown us that several teams that we didn’t expect to be at the top of the European rankings, such as Bayer Leverkusen, Stuttgart, Girone and Hoffenheim, are very realistic in front of goal. It will be interesting to repeat this study at the end of the season to see whether the trends have been confirmed or refuted. We could also carry out in-depth analyses of the position of the teams’ shots and goals.